Calisthenics Skills Temporal Video Segmentation

Antonio Finocchiaro, Giovanni Farinella, Antonino Furnari

2024

Abstract

Calisthenics is a fast-growing bodyweight discipline that consists of different categories, one of which is focused on skills. Skills in calisthenics encompass both static and dynamic elements performed by athletes. The evaluation of static skills is based on their difficulty level and the duration of the hold. Automated tools able to recognize isometric skills from a video by segmenting them to estimate their duration would be desirable to assist athletes in their training and judges during competitions. Although the video understanding literature on action recognition through body pose analysis is rich, no previous work has specifically addressed the problem of calisthenics skill temporal video segmentation. This study aims to provide an initial step towards the implementation of automated tools within the field of Calisthenics. To advance knowledge in this context, we propose a dataset of video footage of static calisthenics skills performed by athletes. Each video is annotated with a temporal segmentation which determines the extent of each skill. We hence report the results of a baseline approach to address the problem of skill temporal segmentation on the proposed dataset. The results highlight the feasibility of the proposed problem, while there is still room for improvement.

Paper Citation

in Harvard Style

Finocchiaro A., Farinella G. and Furnari A. (2024). Calisthenics Skills Temporal Video Segmentation. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 182-190. DOI: 10.5220/0012400600003660

in Bibtex Style

@conference{visapp24,
author={Antonio Finocchiaro and Giovanni Farinella and Antonino Furnari},
title={Calisthenics Skills Temporal Video Segmentation},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2024},
pages={182-190},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012400600003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Calisthenics Skills Temporal Video Segmentation
SN - 978-989-758-679-8
AU - Finocchiaro A.
AU - Farinella G.
AU - Furnari A.
PY - 2024
SP - 182
EP - 190
DO - 10.5220/0012400600003660
PB - SciTePress